Università Roma Tre

Main goal: become familiar with mathematical programming techniques and algorithms to address problems in finance and data analysis.
Specific goals are detailed next according to Dublin descriptors.
- Knowledge and understanding: at the end of the course, students are expected to know the fundamental aspects of mathematical programming theory and algorithms as support to decision making in finance and data analysis.
- Applying knowledge and understanding: at the end of the course, students are expected to know how to rely on mathematical programming tools and methods, and computer software to practically address real-world problems in finance and data analysis.
- Making judgements: the whole course is organized so as to make the students ask (themselves) the “right” questions. To achieve this objective, computer lab activities, exercise sessions, homework assignments, case study analyses are resorted to in a flipped classroom context.
- Communication: students are continuously invited to lead lectures and participate directly and actively in the learning process in flipped classroom schemes.
- Lifelong learning skills: lectures are devised to encourage self-motivated pursuit of knowledge. In fact, as detailed above, but also in the light of an ongoing evaluation approach, students are urged to develop a leading role during the lectures in a cooperative, as well as competitive environment.